Characteristic | Overall, N = 3,0901 | Cases, N = 6801 | Controls, N = 2,4101 | p-value2 |
|---|---|---|---|---|
Sex | 0.498 | |||
Female | 1,317 (42.6%) | 279 (41.0%) | 1,038 (43.1%) | |
Male | 1,772 (57.3%) | 401 (59.0%) | 1,371 (56.9%) | |
(Missing) | 1 (0.0%) | 0 (0.0%) | 1 (0.0%) | |
Age at testing (months) | 0.007 | |||
Mean (SD) | 6.9 (4.0) | 6.5 (3.7) | 7.0 (4.1) | |
Median (IQR) | 6.7 (3.6, 9.7) | 6.1 (3.4, 9.2) | 6.9 (3.7, 9.9) | |
N missing (% missing) | 0.0 (0.0%) | 0.0 (0.0%) | 0.0 (0.0%) | |
Age at testing | 0.016 | |||
under 3m | 620 (20.1%) | 143 (21.0%) | 477 (19.8%) | |
3-6m | 739 (23.9%) | 186 (27.4%) | 553 (22.9%) | |
6-9m | 787 (25.5%) | 169 (24.9%) | 618 (25.6%) | |
9-12m | 569 (18.4%) | 121 (17.8%) | 448 (18.6%) | |
above 1 yo | 375 (12.1%) | 61 (9.0%) | 314 (13.0%) | |
Time tested | <0.001 | |||
Oct-Nov | 814 (26.3%) | 307 (45.1%) | 507 (21.0%) | |
Dec-Jan | 1,246 (40.3%) | 333 (49.0%) | 913 (37.9%) | |
Feb-Mar | 781 (25.3%) | 35 (5.1%) | 746 (31.0%) | |
April and after | 249 (8.1%) | 5 (0.7%) | 244 (10.1%) | |
Race and ethnicity | 0.086 | |||
Hispanic | 1,328 (43.0%) | 280 (41.2%) | 1,048 (43.5%) | |
White non-Hispanic | 820 (26.5%) | 201 (29.6%) | 619 (25.7%) | |
Black non-Hispanic | 533 (17.2%) | 112 (16.5%) | 421 (17.5%) | |
Other non-Hispanic3 | 161 (5.2%) | 26 (3.8%) | 135 (5.6%) | |
unknown | 248 (8.0%) | 61 (9.0%) | 187 (7.8%) | |
Birth weight | 0.067 | |||
Mean (SD) | 3,131.7 (687.3) | 3,182.1 (626.4) | 3,117.8 (702.5) | |
Median (IQR) | 3,214.3 (2,824.9, 3,563.8) | 3,265.0 (2,875.0, 3,576.2) | 3,194.5 (2,805.1, 3,553.6) | |
N missing (% missing) | 783.0 (25.3%) | 184.0 (27.1%) | 599.0 (24.9%) | |
Missing | 783.0 | 184.0 | 599.0 | |
Gestational age | 0.045 | |||
37 weeks or more | 1,915 (62.0%) | 419 (61.6%) | 1,496 (62.1%) | |
Less than 37 weeks | 418 (13.5%) | 76 (11.2%) | 342 (14.2%) | |
(Missing) | 757 (24.5%) | 185 (27.2%) | 572 (23.7%) | |
Pulmonary diseases | 156 (5.0%) | 26 (3.8%) | 130 (5.4%) | 0.099 |
Cardiac diseases | 152 (4.9%) | 30 (4.4%) | 122 (5.1%) | 0.489 |
Anemia | 94 (3.0%) | 15 (2.2%) | 79 (3.3%) | 0.151 |
Having at least one risk factor4 | 750 (24.3%) | 150 (22.1%) | 600 (24.9%) | 0.127 |
Insurance type | 0.092 | |||
private | 983 (31.8%) | 231 (34.0%) | 752 (31.2%) | |
public | 2,088 (67.6%) | 442 (65.0%) | 1,646 (68.3%) | |
uninsured | 19 (0.6%) | 7 (1.0%) | 12 (0.5%) | |
mAb status | <0.001 | |||
No | 2,760 (89.3%) | 659 (96.9%) | 2,101 (87.2%) | |
Yes, 100mg dose | 95 (3.1%) | 6 (0.9%) | 89 (3.7%) | |
Yes, 50mg dose | 235 (7.6%) | 15 (2.2%) | 220 (9.1%) | |
1n (%) | ||||
2Fisher's exact test; Wilcoxon rank sum test; Pearson's Chi-squared test | ||||
3Inclusing Asian, Pacific Islander, Middle Eastern or Northern American, American Indian or Native American by self-reporting. | ||||
4Have at least one of the following conditions recorded in the infant's medical history or diagnosis records: 1) Anemia; 2) Immunodeficiency (e.g. transplantation history, leukemia, etc.); 3) Cardiac diseases (including congenital heart diseases diagnosed at birth or any reporting of heart conditions); 4) Pulmonary diseases; 5) Down syndrome; 6) Small for gestational age (birth weight < 2,500 grams); 7) Prematurity (gestational age less than 37 weeks). | ||||
Here we filtered out only the cases (RSV+) and compare between vaccinated and unvaccinated.
Characteristic | Overall, N = 6801 | Unvaccinated, N = 6591 | Vaccinated, N = 211 |
|---|---|---|---|
Hospital admission | 166 (24.4%) | 161 (24.4%) | 5 (23.8%) |
Duration of hospitalization (days) | |||
Median (IQR) | 1.0 (1.0, 2.0) | 1.0 (1.0, 2.0) | 3.0 (2.0, 3.0) |
N missing (% missing) | 514.0 (75.6%) | 498.0 (75.6%) | 16.0 (76.2%) |
Missing | 514.0 | 498.0 | 16.0 |
ICU admission | 23 (3.4%) | 22 (3.3%) | 1 (4.8%) |
Duration of ICU admission (days) | |||
Median (IQR) | 3.1 (1.8, 7.3) | 3.5 (1.8, 7.4) | 2.4 (2.4, 2.4) |
N missing (% missing) | 657.0 (96.6%) | 637.0 (96.7%) | 20.0 (95.2%) |
Missing | 657.0 | 637.0 | 20.0 |
Required highflow oxygen support | 145 (21.3%) | 141 (21.4%) | 4 (19.0%) |
Distress in URT | 229 (33.7%) | 221 (33.5%) | 8 (38.1%) |
Distress in LRT | 361 (53.1%) | 350 (53.1%) | 11 (52.4%) |
Fever (> 38°C/100.4°F) | 239 (35.1%) | 235 (35.7%) | 4 (19.0%) |
Cough | 98 (14.4%) | 94 (14.3%) | 4 (19.0%) |
Wheezing | 9 (1.3%) | 9 (1.4%) | 0 (0.0%) |
Breathing difficulties | 3 (0.4%) | 3 (0.5%) | 0 (0.0%) |
Bronchiolitis | 349 (51.3%) | 338 (51.3%) | 11 (52.4%) |
Sepsis | 2 (0.3%) | 2 (0.3%) | 0 (0.0%) |
1n (%) | |||
Characteristic | Overall, N = 3,0901 | Immunized, N = 3301 | Unimmunized, N = 2,7601 | p-value2 |
|---|---|---|---|---|
positive_rsv | <0.001 | |||
Cases | 680 (22.0%) | 21 (6.4%) | 659 (23.9%) | |
Controls | 2,410 (78.0%) | 309 (93.6%) | 2,101 (76.1%) | |
Sex | 0.793 | |||
Female | 1,317 (42.6%) | 138 (41.8%) | 1,179 (42.7%) | |
Male | 1,772 (57.3%) | 192 (58.2%) | 1,580 (57.2%) | |
(Missing) | 1 (0.0%) | 0 (0.0%) | 1 (0.0%) | |
Age at testing (months) | <0.001 | |||
Mean (SD) | 6.9 (4.0) | 4.2 (3.1) | 7.2 (4.0) | |
Median (IQR) | 6.7 (3.6, 9.7) | 3.4 (1.8, 6.0) | 7.1 (4.1, 10.0) | |
N missing (% missing) | 0.0 (0.0%) | 0.0 (0.0%) | 0.0 (0.0%) | |
Age at testing | <0.001 | |||
under 3m | 620 (20.1%) | 142 (43.0%) | 478 (17.3%) | |
3-6m | 739 (23.9%) | 105 (31.8%) | 634 (23.0%) | |
6-9m | 787 (25.5%) | 57 (17.3%) | 730 (26.4%) | |
9-12m | 569 (18.4%) | 19 (5.8%) | 550 (19.9%) | |
above 1 yo | 375 (12.1%) | 7 (2.1%) | 368 (13.3%) | |
Time tested | <0.001 | |||
Oct-Nov | 814 (26.3%) | 24 (7.3%) | 790 (28.6%) | |
Dec-Jan | 1,246 (40.3%) | 121 (36.7%) | 1,125 (40.8%) | |
Feb-Mar | 781 (25.3%) | 139 (42.1%) | 642 (23.3%) | |
April and after | 249 (8.1%) | 46 (13.9%) | 203 (7.4%) | |
Race and ethnicity | <0.001 | |||
Hispanic | 1,328 (43.0%) | 138 (41.8%) | 1,190 (43.1%) | |
White non-Hispanic | 820 (26.5%) | 68 (20.6%) | 752 (27.2%) | |
Black non-Hispanic | 533 (17.2%) | 86 (26.1%) | 447 (16.2%) | |
Other non-Hispanic3 | 161 (5.2%) | 24 (7.3%) | 137 (5.0%) | |
unknown | 248 (8.0%) | 14 (4.2%) | 234 (8.5%) | |
Birth weight | <0.001 | |||
Mean (SD) | 3,131.7 (687.3) | 2,938.8 (833.5) | 3,160.9 (657.6) | |
Median (IQR) | 3,214.3 (2,824.9, 3,563.8) | 3,099.6 (2,575.6, 3,483.7) | 3,234.4 (2,875.0, 3,573.7) | |
N missing (% missing) | 783.0 (25.3%) | 26.0 (7.9%) | 757.0 (27.4%) | |
Missing | 783.0 | 26.0 | 757.0 | |
Gestational age | <0.001 | |||
37 weeks or more | 1,915 (62.0%) | 218 (66.1%) | 1,697 (61.5%) | |
Less than 37 weeks | 418 (13.5%) | 90 (27.3%) | 328 (11.9%) | |
(Missing) | 757 (24.5%) | 22 (6.7%) | 735 (26.6%) | |
Pulmonary diseases | 156 (5.0%) | 19 (5.8%) | 137 (5.0%) | 0.534 |
Cardiac diseases | 152 (4.9%) | 33 (10.0%) | 119 (4.3%) | <0.001 |
Anemia | 94 (3.0%) | 11 (3.3%) | 83 (3.0%) | 0.744 |
Having at least one risk factor4 | 750 (24.3%) | 123 (37.3%) | 627 (22.7%) | <0.001 |
Insurance type | <0.001 | |||
private | 983 (31.8%) | 78 (23.6%) | 905 (32.8%) | |
public | 2,088 (67.6%) | 252 (76.4%) | 1,836 (66.5%) | |
uninsured | 19 (0.6%) | 0 (0.0%) | 19 (0.7%) | |
rsv_mab | <0.001 | |||
No | 2,760 (89.3%) | 0 (0.0%) | 2,760 (100.0%) | |
Yes, 100mg dose | 95 (3.1%) | 95 (28.8%) | 0 (0.0%) | |
Yes, 50mg dose | 235 (7.6%) | 235 (71.2%) | 0 (0.0%) | |
1n (%) | ||||
2Pearson's Chi-squared test; Fisher's exact test; Wilcoxon rank sum test | ||||
3Inclusing Asian, Pacific Islander, Middle Eastern or Northern American, American Indian or Native American by self-reporting. | ||||
4Have at least one of the following conditions recorded in the infant's medical history or diagnosis records: 1) Anemia; 2) Immunodeficiency (e.g. transplantation history, leukemia, etc.); 3) Cardiac diseases (including congenital heart diseases diagnosed at birth or any reporting of heart conditions); 4) Pulmonary diseases; 5) Down syndrome; 6) Small for gestational age (birth weight < 2,500 grams); 7) Prematurity (gestational age less than 37 weeks). | ||||
From table1, there are four variables that are significantly different between the case and control (p < 0.05): age tested, month tested, premature (gestage < 37wk), birth weight. But because this is collinearity between prematurity and birth weight, only included one into the model. Because there is less missing in prematurity variable, thus included this instead of birth weight.
## quartz_off_screen
## 2
## quartz_off_screen
## 2
## quartz_off_screen
## 2
Using stepwise (both directions) to select the model using AIC
temp <- df
all_confounders <- c(
"age_at_test_in_months_cat_2", # 3-month interval
"month_when_tested_cat",
"race_ethnicity",
#"risk_factor_gestagelessthan37wks",
"risk_factor_atleastone",
"insurance_type"
)
formula <- as.formula(paste(c("positive_rsv ~ rsv_mab", all_confounders), collapse = " + "))
full.model <- glm(formula, data = temp, family = "binomial")
step.model <- stepAIC(full.model,
scope = list(lower = ~ rsv_mab + age_at_test_in_months_cat_2 + month_when_tested_cat,
upper = formula),
direction = "backward", trace = FALSE)
confounders_infection <- c(
"age_at_test_in_months_cat_2", # 3-month interval
"month_when_tested_cat"
)
step.model$anova
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## positive_rsv ~ rsv_mab + age_at_test_in_months_cat_2 + month_when_tested_cat +
## race_ethnicity + risk_factor_atleastone + insurance_type
##
## Final Model:
## positive_rsv ~ rsv_mab + age_at_test_in_months_cat_2 + month_when_tested_cat
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 3074 2821.033 2853.033
## 2 - race_ethnicity 4 3.9353263 3078 2824.968 2848.968
## 3 - insurance_type 2 1.8446735 3080 2826.813 2846.813
## 4 - risk_factor_atleastone 1 0.2421708 3081 2827.055 2845.055
aic.infection <- as.data.frame(step.model$anova) %>% dplyr::select(Step, AIC) %>%
mutate(endpoint = "Medically attended RSV infection") %>%
mutate(Step = ifelse(Step == "", str_replace(as.character(formula[3]), "rsv_mab \\+ ", ""), Step)) %>%
rename(Confounders = Step)
temp <- df %>% filter(encounter_type == "outpatient")
full.model <- glm(formula, data = temp, family = "binomial")
step.model <- stepAIC(full.model,
scope = list(lower = ~ rsv_mab + age_at_test_in_months_cat_2 + month_when_tested_cat,
upper = formula),
direction = "backward", trace = FALSE)
confounders_ed <- c(
"age_at_test_in_months_cat_2", # 3-month interval
"month_when_tested_cat"
)
step.model$anova
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## positive_rsv ~ rsv_mab + age_at_test_in_months_cat_2 + month_when_tested_cat +
## race_ethnicity + risk_factor_atleastone + insurance_type
##
## Final Model:
## positive_rsv ~ rsv_mab + age_at_test_in_months_cat_2 + month_when_tested_cat
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 2489 2209.986 2241.986
## 2 - race_ethnicity 4 4.099769 2493 2214.086 2238.086
## 3 - insurance_type 2 2.227762 2495 2216.313 2236.313
## 4 - risk_factor_atleastone 1 1.303477 2496 2217.617 2235.617
aic.ed <- as.data.frame(step.model$anova) %>% dplyr::select(Step, AIC) %>%
mutate(endpoint = "RSV-associated outpatient visit") %>%
mutate(Step = ifelse(Step == "", str_replace(as.character(formula[3]), "rsv_mab \\+ ", ""), Step)) %>%
rename(Confounders = Step)
temp <- df %>% filter(encounter_type == "inpatient")
full.model <- glm(formula, data = temp, family = "binomial")
step.model <- stepAIC(full.model,
scope = list(lower = ~ rsv_mab + age_at_test_in_months_cat_2 + month_when_tested_cat,
upper = formula),
direction = "backward", trace = FALSE)
confounders_inpatient <- c(
"age_at_test_in_months_cat_2", # 3-month interval
"month_when_tested_cat",
"risk_factor_atleastone"
)
step.model$anova
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## positive_rsv ~ rsv_mab + age_at_test_in_months_cat_2 + month_when_tested_cat +
## race_ethnicity + risk_factor_atleastone + insurance_type
##
## Final Model:
## positive_rsv ~ rsv_mab + age_at_test_in_months_cat_2 + month_when_tested_cat +
## risk_factor_atleastone
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 528 525.1960 555.1960
## 2 - race_ethnicity 4 4.9228875 532 530.1189 552.1189
## 3 - insurance_type 1 0.2574532 533 530.3764 550.3764
aic.inpatient <- as.data.frame(step.model$anova) %>% dplyr::select(Step, AIC) %>%
mutate(endpoint = "RSV-associated hospitalization") %>%
mutate(Step = ifelse(Step == "", str_replace(as.character(formula[3]), "rsv_mab \\+ ", ""), Step)) %>%
rename(Confounders = Step)
temp <- df %>% filter(icu_admitted == "yes" | highflow_oxygen == 1)
full.model <- glm(formula, data = temp, family = "binomial")
step.model <- stepAIC(full.model,
scope = list(lower = ~ rsv_mab + age_at_test_in_months_cat_2 + month_when_tested_cat,
upper = formula),
direction = "backward", trace = FALSE)
confounders_severe <- c(
"age_at_test_in_months_cat_2", # 3-month interval
"month_when_tested_cat",
"risk_factor_atleastone"
)
step.model$anova
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## positive_rsv ~ rsv_mab + age_at_test_in_months_cat_2 + month_when_tested_cat +
## race_ethnicity + risk_factor_atleastone + insurance_type
##
## Final Model:
## positive_rsv ~ rsv_mab + age_at_test_in_months_cat_2 + month_when_tested_cat +
## risk_factor_atleastone
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 420 395.3552 425.3552
## 2 - race_ethnicity 4 2.127287 424 397.4825 419.4825
## 3 - insurance_type 1 1.530426 425 399.0129 419.0129
aic.severe <- as.data.frame(step.model$anova) %>% dplyr::select(Step, AIC) %>%
mutate(endpoint = "RSV-associated severe outcomes") %>%
mutate(Step = ifelse(Step == "", str_replace(as.character(formula[3]), "rsv_mab \\+ ", ""), Step)) %>%
rename(Confounders = Step)
temp <- df %>% filter(symp_LRT_distress == 1)
full.model <- glm(formula, data = temp, family = "binomial")
step.model <- stepAIC(full.model,
scope = list(lower = ~ rsv_mab + age_at_test_in_months_cat_2 + month_when_tested_cat,
upper = formula),
direction = "backward", trace = FALSE)
confounders_LRTI <- c(
"age_at_test_in_months_cat_2", # 3-month interval
"month_when_tested_cat",
"risk_factor_atleastone"
)
step.model$anova
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## positive_rsv ~ rsv_mab + age_at_test_in_months_cat_2 + month_when_tested_cat +
## race_ethnicity + risk_factor_atleastone + insurance_type
##
## Final Model:
## positive_rsv ~ rsv_mab + age_at_test_in_months_cat_2 + month_when_tested_cat +
## risk_factor_atleastone
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 727 804.7157 836.7157
## 2 - race_ethnicity 4 2.009427 731 806.7251 830.7251
## 3 - insurance_type 2 2.671050 733 809.3961 829.3961
aic.LRTI <- as.data.frame(step.model$anova) %>% dplyr::select(Step, AIC) %>%
mutate(endpoint = "RSV-associated LRTI") %>%
mutate(Step = ifelse(Step == "", str_replace(as.character(formula[3]), "rsv_mab \\+ ", ""), Step)) %>%
rename(Confounders = Step)
temp <- df %>% filter(symp_LRT_distress == 1 & encounter_type == "inpatient")
full.model <- glm(formula, data = temp, family = "binomial")
step.model <- stepAIC(full.model,
scope = list(lower = ~ rsv_mab + age_at_test_in_months_cat_2 + month_when_tested_cat,
upper = formula),
direction = "backward", trace = FALSE)
confounders_LRTIhosp <- c(
"age_at_test_in_months_cat_2", # 3-month interval
"month_when_tested_cat",
"risk_factor_atleastone"
)
step.model$anova
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## positive_rsv ~ rsv_mab + age_at_test_in_months_cat_2 + month_when_tested_cat +
## race_ethnicity + risk_factor_atleastone + insurance_type
##
## Final Model:
## positive_rsv ~ rsv_mab + age_at_test_in_months_cat_2 + month_when_tested_cat +
## risk_factor_atleastone
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 275 267.6900 297.6900
## 2 - race_ethnicity 4 4.245335 279 271.9354 293.9354
## 3 - insurance_type 1 1.056593 280 272.9920 292.9920
aic.LRTIhosp <- as.data.frame(step.model$anova) %>% dplyr::select(Step, AIC) %>%
mutate(endpoint = "RSV-associated LRTI hospitalization") %>%
mutate(Step = ifelse(Step == "", str_replace(as.character(formula[3]), "rsv_mab \\+ ", ""), Step)) %>%
rename(Confounders = Step)
endpoint | Confounders | AIC |
|---|---|---|
Medically attended RSV infection | age_tested + month_tested + race_ethnicity + atleastone_risk_factor + insurance_type | 2,853.0 |
- race_ethnicity | 2,849.0 | |
- insurance_type | 2,846.8 | |
- atleastone_risk_factor | 2,845.1 | |
RSV-associated outpatient visit | age_tested + month_tested + race_ethnicity + atleastone_risk_factor + insurance_type | 2,242.0 |
- race_ethnicity | 2,238.1 | |
- insurance_type | 2,236.3 | |
- atleastone_risk_factor | 2,235.6 | |
RSV-associated hospitalization | age_tested + month_tested + race_ethnicity + atleastone_risk_factor + insurance_type | 555.2 |
- race_ethnicity | 552.1 | |
- insurance_type | 550.4 | |
RSV-associated severe outcomes | age_tested + month_tested + race_ethnicity + atleastone_risk_factor + insurance_type | 425.4 |
- race_ethnicity | 419.5 | |
- insurance_type | 419.0 | |
RSV-associated LRTI | age_tested + month_tested + race_ethnicity + atleastone_risk_factor + insurance_type | 836.7 |
- race_ethnicity | 830.7 | |
- insurance_type | 829.4 | |
RSV-associated LRTI hospitalization | age_tested + month_tested + race_ethnicity + atleastone_risk_factor + insurance_type | 297.7 |
- race_ethnicity | 293.9 | |
- insurance_type | 293.0 |
Look at when restricting to records up to March, if the results are
the same as what was reported in the ACIP meeting by CDC.
Characteristic | Overall, N = 3301 | 100mg, N = 951 | 50mg, N = 2351 | p-value2 |
|---|---|---|---|---|
Sex | 0.176 | |||
Female | 138 (41.8%) | 34 (35.8%) | 104 (44.3%) | |
Male | 192 (58.2%) | 61 (64.2%) | 131 (55.7%) | |
(Missing) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | |
Age at testing (months) | <0.001 | |||
Mean (SD) | 4.2 (3.1) | 7.5 (2.7) | 2.9 (2.0) | |
Median (IQR) | 3.4 (1.8, 6.0) | 7.3 (5.6, 8.9) | 2.4 (1.5, 3.8) | |
N missing (% missing) | 0.0 (0.0%) | 0.0 (0.0%) | 0.0 (0.0%) | |
Age at testing | <0.001 | |||
under 3m | 142 (43.0%) | 2 (2.1%) | 140 (59.6%) | |
3-6m | 105 (31.8%) | 28 (29.5%) | 77 (32.8%) | |
6-9m | 57 (17.3%) | 42 (44.2%) | 15 (6.4%) | |
9-12m | 19 (5.8%) | 17 (17.9%) | 2 (0.9%) | |
above 1 yo | 7 (2.1%) | 6 (6.3%) | 1 (0.4%) | |
Time tested | 0.010 | |||
Oct-Nov | 24 (7.3%) | 14 (14.7%) | 10 (4.3%) | |
Dec-Jan | 121 (36.7%) | 34 (35.8%) | 87 (37.0%) | |
Feb-Mar | 139 (42.1%) | 36 (37.9%) | 103 (43.8%) | |
April and after | 46 (13.9%) | 11 (11.6%) | 35 (14.9%) | |
Race and ethnicity | 0.004 | |||
Hispanic | 138 (41.8%) | 34 (35.8%) | 104 (44.3%) | |
White non-Hispanic | 68 (20.6%) | 15 (15.8%) | 53 (22.6%) | |
Black non-Hispanic | 86 (26.1%) | 26 (27.4%) | 60 (25.5%) | |
Other non-Hispanic3 | 24 (7.3%) | 15 (15.8%) | 9 (3.8%) | |
unknown | 14 (4.2%) | 5 (5.3%) | 9 (3.8%) | |
Birth weight | 0.875 | |||
Mean (SD) | 2,938.8 (833.5) | 2,893.7 (1,018.0) | 2,957.5 (745.9) | |
Median (IQR) | 3,099.6 (2,575.6, 3,483.7) | 3,114.4 (2,545.6, 3,503.8) | 3,064.6 (2,580.5, 3,443.8) | |
N missing (% missing) | 26.0 (7.9%) | 6.0 (6.3%) | 20.0 (8.5%) | |
Missing | 26.0 | 6.0 | 20.0 | |
Gestational age | 0.061 | |||
37 weeks or more | 218 (66.1%) | 62 (65.3%) | 156 (66.4%) | |
Less than 37 weeks | 90 (27.3%) | 31 (32.6%) | 59 (25.1%) | |
(Missing) | 22 (6.7%) | 2 (2.1%) | 20 (8.5%) | |
Pulmonary diseases | 19 (5.8%) | 11 (11.6%) | 8 (3.4%) | 0.004 |
Cardiac diseases | 33 (10.0%) | 7 (7.4%) | 26 (11.1%) | 0.311 |
Anemia | 11 (3.3%) | 6 (6.3%) | 5 (2.1%) | 0.084 |
Having at least one risk factor4 | 123 (37.3%) | 40 (42.1%) | 83 (35.3%) | 0.248 |
Insurance type | 0.775 | |||
private | 78 (23.6%) | 21 (22.1%) | 57 (24.3%) | |
public | 252 (76.4%) | 74 (77.9%) | 178 (75.7%) | |
uninsured | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | |
1n (%) | ||||
2Fisher's exact test; Wilcoxon rank sum test; Pearson's Chi-squared test | ||||
3Inclusing Asian, Pacific Islander, Middle Eastern or Northern American, American Indian or Native American by self-reporting. | ||||
4Have at least one of the following conditions recorded in the infant's medical history or diagnosis records: 1) Anemia; 2) Immunodeficiency (e.g. transplantation history, leukemia, etc.); 3) Cardiac diseases (including congenital heart diseases diagnosed at birth or any reporting of heart conditions); 4) Pulmonary diseases; 5) Down syndrome; 6) Small for gestational age (birth weight < 2,500 grams); 7) Prematurity (gestational age less than 37 weeks). | ||||
Per ACIP’s meeting slides, it would be good to look at VE among <8 months and 8-12 months. Should we classify age using age when being tested or age when nirsevimab became available (chose this).
library(epiR)
# for medically attended RSV infection
epi.sscc(N = 3090, # total number of subjects included
OR = NA, # expected study odds ratio
p1 = 0.031, # the prevalence of exposure amongst the cases.
p0 = 0.128, # the prevalence of exposure amongst the controls.
n = 3090, # the total number of subjects in the study (i.e., the number of cases plus the number of controls). what is the difference from N??
power = 0.8, # the required study power. ( Ideally, minimum power of a study required is 80%.)
r = 3.54, # number in the control group divided by number in the case group. In our study: 2410/680
sided.test = 2, # use a one- or two-sided test
nfractional = FALSE, # whether to return fractional sample size
conf.level = 0.95,
method = "unmatched")
## $n.total
## [1] 3090
##
## $n.case
## [1] 681
##
## $n.control
## [1] 2409
##
## $power
## [1] 0.8
##
## $OR
## [1] 0.6621704 1.3936692
# returning: OR = 0.662: the expected detectable odds ratio given the number of study subjects.
# VE = 1-OR = 0.338
# for RSV outpatient
epi.sscc(N = 2505, # total number of subjects included
OR = NA, # expected study odds ratio
p1 = 0.031, # the prevalence of exposure amongst the cases.
p0 = 0.116, # the prevalence of exposure amongst the controls.
n = 2050, # the total number of subjects in the study (i.e., the number of cases plus the number of controls). what is the difference from N??
power = 0.8, # the required study power. ( Ideally, minimum power of a study required is 80%.)
r = 3.87, # number in the control group divided by number in the case group. here:
sided.test = 2, # use a one- or two-sided test
nfractional = FALSE, # whether to return fractional sample size
conf.level = 0.95,
method = "unmatched")
## $n.total
## [1] 2050
##
## $n.case
## [1] 421
##
## $n.control
## [1] 1629
##
## $power
## [1] 0.8
##
## $OR
## [1] 0.5629488 1.5267781
# returning: OR = 0.5629488: the expected detectable odds ratio given the number of study subjects.
# VE = 1-OR = 0.4370512
# for RSV inpatient
epi.sscc(N = 543, # total number of subjects included
OR = NA, # expected study odds ratio
p1 = 0.0301, # the prevalence of exposure amongst the cases.
p0 = 0.1856764, # the prevalence of exposure amongst the controls.
n = 2050, # the total number of subjects in the study (i.e., the number of cases plus the number of controls). what is the difference from N??
power = 0.8, # the required study power. ( Ideally, minimum power of a study required is 80%.)
r = 2.27, # number in the control group divided by number in the case group. here:
sided.test = 2, # use a one- or two-sided test
nfractional = FALSE, # whether to return fractional sample size
conf.level = 0.95,
method = "unmatched")
## $n.total
## [1] 2050
##
## $n.case
## [1] 627
##
## $n.control
## [1] 1423
##
## $power
## [1] 0.8
##
## $OR
## [1] 0.6856783 1.3819886
# returning: OR = 0.6856783: the expected detectable odds ratio given the number of study subjects.
# VE = 1-OR = 0.3143217
# for severe outcomes
epi.sscc(N = 435, # total number of subjects included
OR = NA, # expected study odds ratio
p1 = 0.02758621, # the prevalence of exposure amongst the cases.
p0 = 0.2689655, # the prevalence of exposure amongst the controls.
n = 435, # the total number of subjects in the study (i.e., the number of cases plus the number of controls). what is the difference from N??
power = 0.8, # the required study power. ( Ideally, minimum power of a study required is 80%.)
r = 2, # number in the control group divided by number in the case group. here:
sided.test = 2, # use a one- or two-sided test
nfractional = FALSE, # whether to return fractional sample size
conf.level = 0.95,
method = "unmatched")
## $n.total
## [1] 435
##
## $n.case
## [1] 145
##
## $n.control
## [1] 290
##
## $power
## [1] 0.8
##
## $OR
## [1] 0.4767214 1.8202937
# returning: OR = 0.4767214, the expected detectable odds ratio given the number of study subjects.
# VE = 1-OR = 0.5232786